Trajectory Planning for a Pseudo Omnidirectional Vehicle using Particle Swarm Optimization

We propose an online motion planning approach for a pseudo omnidirectional vehicle based on particle swarm optimization. Therefore, we first describe the principles behind the optimization process. Afterwards we derive representations for the vehicle’s movement based on the description of the position of the instantaneous center of motion. Then, the mathematical operators used in the optimization process for the trajectories are described with regard to the previously derived representations. The costfunction is explained with focus on the new opportunities in the movement of the vehicle like e.g. driving sideways. However, the extra degree of freedom not only brings benefits, but also complicates the generation of trajectories for the initial particle swarm. Therefore we describe how to efficiently sample omnidirectional trajectories and also trajectories for certain well known gaits like Ackermann driving. Finally, the approach is evaluated in simulation showing the full maneuverability of the vehicle.